• Data Science; Concepts and Practice (Paperback) 9780128147610
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Data Science
Concepts and Practice

Fully updated edition on data science concepts, strategies and techniques using open source software, RapidMiner

Vijay Kotu (Author), Bala Deshpande (Author)

9780128147610, Elsevier Science

Paperback, published 3 December 2018

568 pages
23.4 x 19 x 3.5 cm, 1.09 kg

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.

Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data.

You’ll be able to:

  1. Gain the necessary knowledge of different data science techniques to extract value from data.

  2. Master the concepts and inner workings of 30 commonly used powerful data science algorithms.

  3. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform

Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...

1. Introduction
2. Data Science Process
3. Data Exploration
4. Classification
5. Deep Learning
6. Regression Methods
7. Association Analysis
8. Recommendation Engines
9. Clustering
10. Text Mining (renamed to: Natural Language Processing)
11. Time Series Forecasting
12. Anomaly Detection
13. Feature Selection
14. Model Evaluation
15. Efficient Model Execution
16. Getting Started with RapidMiner

Subject Areas: Machine learning [UYQM], Information technology: general issues [UB]
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